Mastering Data Analysis With Python Pandas & Matplotlib 2018

Mastering Data Analysis With Python Pandas & Matplotlib 2018
Mastering Data Analysis With Python Pandas & Matplotlib 2018

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 4.5 Hours | 521 MB
eLearning | Skill level: All Levels


Learn Python Pandas and Matplotlib and Start your career in Data Analysis without prior knowledge required!

Welcome! “Mastering Data Analysis With Python Pandas & Matplotlib 2018” is an excellent choice for both beginners and experts looking to expand their knowledge in Machine Learning field.

Data Analysis is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.

Mastering Data Analysis With Python Pandas & Matplotlib 2018 offers in-depth video tutorials in which we’ll dive into tons of different datasets, short and long, broken and pristine. I’ll take you step-by-step through Data Analysis process using the most powerful python libraries (Numpy, Pandas and Matplotlib), from installation to visualization! . tutorials include:

  • Installing.
  • Creating.
  • Accessing.
  • Applying arithmetic operations.
  • Reindexing.
  • Slicing.
  • Tidying up.
  • Handling missing data.
  • Handling duplicated data.
  • Concatenating.
  • Grouping.
  • Aggregating.
  • deleting.
  • visualizing.
  • and more!

Whether you’re a newbie or an expert,Mastering Data Analysis With Python Pandas & Matplotlib 2018 will take your career to the next level,So stand out from the crowd and advance your career now!

+ Table of Contents

Introduction
1 Intro
2 Downloading Anaconda and Jupyter overview

Series Object
3 Creating Series Object
4 Information about Series Object
5 Peeking at data
6 Accessing data with loc, iloc and ix parameters
7 Applying arithmetic operations on Series Object
8 Reindexing the Series Object
9 Slicing Series Object

DataFrame
10 Creating DataFrame Object
11 Operations on the DataFrame Columns
12 Selecting rows of DataFrame and Scalar Lookup
13 Modifying DataFrame
14 Modifying DataFrame2
15 Arithmetic Operations
16 Hierarchical index and reindexing

Accessing Data
17 Importing Data
18 Exporting Data

Tidy Data
19 Tidying up data
20 Dealing with missing data 1
21 Dealing with missing data 2
22 Dealing with missing data 3
23 Duplicated data
24 How to tidy data up

Combining Data
25 Concatenation
26 Merging Data 1
27 Merging Data 2

Grouping and Aggregating
28 Intro to SAC
29 Grouping Data 1
30 Grouping Data 2
31 Apply – Aggregation
32 Apply – Transformation
33 Apply – Filtering

Time-Series Object
34 Intro to time-series object
35 Time-Series Object 1
36 Time-Series Object 2
37 Time-Series Object 3
38 Time-Series Object 4

Data Visualization using Matplotlib
39 Intro to Matplotlib
40 Matplotlib 1
41 Matplotlib 2


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